@conference{2026790902016,
author = "Sun, ShengJing and Iglesias, Carlos A. and Zhan Liu and Nicole Glassey Balet",
abstract = "With the travel and tourism industry flourishing worldwide, it is vital
for tourism supplier and markers to understand tourist traits aiming
to target consumers and assist decision makings. However,
traditional tourist analysis methods, e.g. questionnaire survey, are
labor-intensive and time-consuming. With the development of
social networks, tourists publish large quantities of travel
experiences on them, which enables to discover traditional tourist
traits and more new traits which could not be obtained from
traditional questionnaires. In this paper, we design a methodology
for tourism traits analysis from social networks, which is based on
the social learning theory. The methodology includes three
components: tourist demographic analysis, tourist social influences
analysis, and tourist behaviour analysis. For demographic analysis,
it comprises the analysis of gender, age, location, education,
profession, and interests for tourists. Regarding social influences
analysis, it contains the analysis of follower count, post count,
account type, and follower/followee ratio of tourists. The analysis of
post pattern, travel frequency, type of tourism-related products, and
top visited destinations consists of tourist behaviour analysis. We
conduct a case study, which is related on the Chinese tourists
toward Switzerland based on our methodology and social media
big data analysis from Sina Weibo. The concerning data is
collected by project SWICICO from HES-SO Valais in Switzerland.
Different significant findings are obtained and some examples are
given: the Chinese tourists in Switzerland tend to be young people,
are likely to have college experiences. They have interests in
travel, sport, art, and education etc, and most of them are from
higher economic developed cities. They tend to have higher social
influences. Besides, they tend to travel to Switzerland in June, July,
August, October and February, and most of them are their first-time
travel. The top visited destinations are Jungfrau, Interlaken, Zurich
and so on, top-ranking products and services are chocolate,
cheese, exhibition in Basel, auto show etc. Those findings could
empower tourism suppliers and markers to better align the market
efforts while making lasting, meaningful market strategies. And our
proposed methodology could be applied to analyse tourist traits in
any social network platform. ",
booktitle = "Proceedings of the International Conference of Tourism (ICOT 2016)",
keywords = "tourist;chinese;traits;switzerland",
month = "June",
title = "{T}ourists traits analysis on social networks ",
year = "2016",
}